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Despite their success for object detection, convolutional neural networks are ill-equipped for incremental learning, i.e., adapting the original model trained on a set of classes to additionally detect objects of new classes, in the absence of the initial training data. They suffer from "catastrophic forgetting"-an abrupt degradation of performance on the original set of classes, when the training objective is adapted to the new classes. We present a method to address this issue, and learndoi:10.1109/iccv.2017.368 dblp:conf/iccv/ShmelkovSA17 fatcat:j24eunukrzannbd6aummmfb47y